The knowledge-based benefits resulting from the use of artificial intelligence, machine learning, and data science and visualization tools in education research remain conditioned on computer programing expertise. Democratizing Data Science (DDS), a new data analytics movement, frees these benefits by lifting computer programming restrictions and offering open software access to conduct qualitative and mixed method research. This course constitutes the first product released as part of the mission of DDS.

This course introduces an analytic framework and free software for dynamically capturing the evolution of knowledge prospectively (i.e., ethnographic or extended on-site observations) or retrospectively (i.e., interviews, essays, social media posts). Mapping, Organizing, and Visualizing Interdependent Events (MOVIE) represents a breakthrough in how qualitative researchers can examine dynamic phenomena. MOVIE enables researchers to study how temporal elements and events in synchronous (e.g., focus group discussions) and asynchronous (e.g., interviews) research settings evolve while adding contextual information in real time to strengthen our understandings of the phenomena studied. In line with the DDS mission, this course guarantees that all participants, regardless of computer programming expertise will be able to benefit from the data mining and visualization tools provided by MOVIE, either using their own data or data provided by the instructor. 

  
RL2021-2
Why Aren't You Writing? Clearing Obstacles to Productivity
Tuesday, June 8, 2021
  
INSTRUCTOR

Sharon Zumbrunn, Virginia Commonwealth University    
						

Appropriate for graduate students and seasoned academics, this hands-on course will be a straightforward guide to helping participants begin to understand and overcome the psychological, emotional, and logistical hurdles that can get in their way of being productive writers. Specifically, this course will intertwine a discussion of the research underlying the ways academic writers often sabotage their success with practical strategies designed to help session participants build a healthier relationship with writing to ultimately write more with less pain.

This course is divided into three primary sections: 1) Get Priorganized! Planning a Realistic Schedule for Your Writing, 2) Unstuck: Overcoming Obstacles to Progress, and 3) Finding Your Writing Mojo and Staying Motivated. Participants will begin by defining (or perhaps REdefining) their writing and wellness goals, systematically prioritizing and breaking each into manageable and realistic tasks. Participants will then identify obstacles in the way of their writing progress and define behaviors that can build a flexible writing habit to maximize time, space, and grace. Participants will also identify and harness aspects of their writing under their control. We will identify how and when maladaptive perfectionism tendencies can stand in way of progress, including how our inner critic can hijack our writing progress, and practice strategies for approaching writing in constructive ways. The course wraps up as participants make a plan to build their personal writing village for sustainable and continued support in their writing. All materials and additional resources are provided.

  
RL2021-3
Using R Software for Item Response (IRT) Model Calibrations
Tuesday, June 15, 2021
  
INSTRUCTORS
						

This interactive training course will introduce the concepts of unidimensional IRT models and provide instruction, demonstration, and hands-on opportunities of using the free R software to estimate commonly used IRT models. Participants will receive a discount code for Using R for Item Response Theory Model Applications, written by the course instructors.

Concepts of commonly used unidimensional IRT models will be taught (e.g. Rasch, 1PL, 2PL, 3PL, GR, and GPC), with little focus on statistical theory. Participants will receive detailed training on how to correctly execute the R IRT packages and interpret the results, with ample opportunities for hands-on analysis. Example datasets will be provided for practical applications.

The target audience for this course includes graduate students, practitioners, and researchers interested in advancing their knowledge of IRT and enhancing skills of using R to do IRT analysis. A basic understanding of IRT is highly recommended. Prior knowledge of R is not required. Familiarity with writing syntax may also be helpful for using R but is not essential. Participants should bring their own laptop with the free R software and packages installed. Instructions for downloading R and installing the necessary packages will be provided prior to the course. Participants may also bring their own dataset for more hands-on assistance.

  
RL2021-4
Analyzing Large-Scale Assessment Data Using R
Tuesday, June 22, 2021
 
INSTRUCTORS
						

Emmanuel Sikali, National Center for Education Statistics (course director)

Paul Bailey, American Institutes for Research

Ting Zhang, American Institutes for Research
 

Martin Hooper, American Institutes for Research 

Michael Lee, American Institutes for Research 

Yuqi Liao, American Institutes for Research

This course will introduce the unique design features of large-scale assessment data and provide guidance in data analysis strategies, including the selection and use of appropriate plausible values, sampling weights, and variance estimation procedures (i.e., jackknife approaches). The course will provide participants with training virtually in analyzing public-use NAEP or TIMSS data files using the R package EdSurvey, which was developed for analyzing national and international large-scale assessment data with complex psychometric and sampling designs. Participants will learn how to perform:
 

• data manipulation,
• descriptive statistics
• cross tabulation and plausible value means,
• achievement levels,
• percentiles, and
• linear and logistic regression.

The knowledge and analytic approach learned from this course can be applied to analyzing other large-scale national and international data with plausible values. This course is designed for individuals in government, universities, private sector, and nonprofit organizations who are interested in learning how to analyze large-scale assessment data with plausible values. Participants should have at least basic knowledge of R software (e.g., took an entry level training on R programming) as well as statistical techniques including statistical inference and multiple regression. Having working knowledge of Item Response Theory and sampling theory would help but not required. Participants need to have a computer preloaded with the latest version of the R and RStudio software to practice the analysis.

  
RL2021-5
Designing Adequately Powered Cluster and Multisite Randomized Trials to Detect Main Effects, Moderation, and Mediation
Wednesday, July 7, 2021
  
INSTRUCTORS
						

  
RL2021-6
Multimodal Analysis and Social Semiotics for Qualitative Analysis in Educational Research
Thursday, July 8, 2021
  
INSTRUCTORS
						

Mary McVee, University at Buffalo, SUNY 

Ryan Rish, University at Buffalo, SUNY 

Angel Lin, Simon Fraser University

Qinghua Chen, Simon Fraser University
  

Recent developments in qualitative research include increasing analysis of multimodality. This course introduces scholars to multimodal analysis via social semiotics using diverse perspectives from multimodality and narrative, frame analysis, and nexus analysis. Course objectives include introduction to social semiotics and multimodality, basic techniques in analysis, and considerations of the role of theory. The target audience is graduate students, early career scholars, and advanced researchers who may have limited knowledge of multimodality and social semiotics and seek to learn about theories and analysis related to multimodality. Brief lectures provide an overview of theory, analytic techniques, or approaches. Workshop style interactions and analysis of data or examples will then be provided by the instructors. At minimum to develop a common starting point, before the conference, participants are encouraged to read: New London Group (1996). A pedagogy of multiliteracies: Designing social futures. Harvard Educational Review, 66(1), 60-92. However, the course will also be suitable for those who already have some foundation in social semiotics and multimodality due to the nature of the hands-on analysis.